Optimal instructional policies based on a random-trial incremental model of learning

The random-trial incremental (RTI) model of human associative learning proposes that learning due to a trial where the association is presented proceeds incrementally; but with a certain probability, constant across trials, no learning occurs due to a trial. Based on RTI, identifying a policy for se...

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Veröffentlicht in:IEEE transactions on systems, man and cybernetics. Part A, Systems and humans man and cybernetics. Part A, Systems and humans, 2000-07, Vol.30 (4), p.490-494
1. Verfasser: Katsikopoulos, K.V.
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description The random-trial incremental (RTI) model of human associative learning proposes that learning due to a trial where the association is presented proceeds incrementally; but with a certain probability, constant across trials, no learning occurs due to a trial. Based on RTI, identifying a policy for sequencing presentation trials of different associations for maximizing overall learning can be accomplished via a Markov decision process. For both finite and infinite horizons and a quite general structure of costs and rewards, a policy that on each trial presents an association that leads to the maximum expected immediate net reward is optimal.
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subjects Cost function
Cybernetics
Human
Humans
Industrial engineering
Infinite horizon
Learning
Learning systems
Markov processes
Mathematical analysis
Mathematical model
Optimization
Policies
Predictive models
Psychology
Random variables
Sequencing
Testing
title Optimal instructional policies based on a random-trial incremental model of learning
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